Cognitive Radio-Enabled Dynamic Spectrum Access Framework for High-Density IoT Sensor Deployments

Abstract

The exploratory growth in the number of the Internet of Things (IoT) devices has created more congestion than ever in the unlicensed Industrial, Scientific and Medical (ISM) bands, causing compromised Quality of Service (QoS) and the inability to scale the network. In this paper, I suggest a Cognitive Radio (CR)-based Dynamic Spectrum Access (DSA) architecture that could be applicable to high-density sensor networks. The framework adopts a simple Energy Detection (ED) sensing principle, so that the IoT sensors can be able to automatically discover and use the spectrum holes of licenced primary bands without inducing destructive interference. We come up with an adaptive thresholding algorithm so as to maximize the innate sensing-throughput tradeoff, which takes into consideration the changing noise floors in dense urban deployments. Simulation of the evaluation of performance is done over a high level of simulations based on Spectral Efficiency and Aggregate Network Throughput. Findings show that the resulting spectrum utilisation is substantially higher than that of the usual techniques of performing the assignments in a static manner. In particular, the model ensures a high throughput with a dense node area and is somewhat effective in eliminating the hidden node issue and the chances of collision witnessed in massive IoT networks. The study has offered an architectural basis that can be extended to the 6G-powered IoT ecosystems in future where efficient, non-intrusive coexistence between the spectrums is the key factor to sustainable connectivity.